Project Summary/Abstract Alternative polyadenylation (APA) is an RNA-processing mechanism that generate diverse 3? termini to have distinct protein coding regions; or contain different cis-regulatory elements, like miRNA binding sites, to influence stability, translation and localization. APA is emerging as a new player in posttranscriptional gene expression regulation and is estimated to affect more than 70% of human genes. Both proliferating and transformed cells have been shown to favor shortened 3? UTRs, leading to activation of transcription factors and proto-oncogenes through escaping miRNA-mediated repression. Accumulating evidence has indicated that APA is tightly regulated and play important physiological roles in blood cell differentiation and blood disorders. For example, shorter 3? UTRs are preferred during immune cell activation. However, there is NO dedicated APA study targeting the blood system to investigate the critical APA genes, the functional consequences of APA and the mechanisms governing APA. The main obstacle is that polyA profiling methods have not been widely adopted. In contrast, an ever-growing number of RNA-seq datasets have been generated for gene expression analysis in blood related cells, but not been analyzed for APA purpose. By far, we have collected 2,642 RNA-seq samples related to the blood including different blood cell types like B-cell, T-cell, human hematopoietic stem cell, lymphocytes and granulocytes. Meanwhile, we developed a novel bioinformatics tool, namely DaPars, for Dynamic analysis of Alternative PolyAdenylation from RNA-seq (Nature Commun. 2014). In this proposal, in order to fill the above knowledge gap between APA and hematology, we set out to generate a focused APA atlas for cells of the hematopoietic system from public datasets, a so-called Hematology APA Atlas (HaemAPA), by applying DaPars to existing RNA-seq of ~2,700 blood related samples, which will be the largest and most dedicated APA landscape for hematopoietic system. Then, we will develop and apply a network-guided low-rank based regression model, a novel in silico APA regulator screening framework, to identify underling APA regulators. All the results will be released in a web data portal called HaemAPA. HaemAPA will provide a valuable resource for the wider scientific community to pursue a multitude of studies about APA in hematology, that has not previously been possible due to limited quantification of APA usages in the blood system. By doing so, HaemAPA will shed light on the blood cell fate determination during haemopoiesis and help us understand the mechanism of complex diseases from a new direction, and to develop novel targeted therapeutics for blood diseases.